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Exposure, hazard, and survival analysis of diffusion on social networks
dc.contributor.authorWu, Jiacheng
dc.contributor.authorCrawford, Forrest W.
dc.contributor.authorKim, David A.
dc.contributor.authorStafford, Derek
dc.contributor.authorChristakis, Nicholas A.
dc.date.accessioned2018-07-13T15:48:07Z
dc.date.available2019-09-04T20:15:39Zen
dc.date.issued2018-07-30
dc.identifier.citationWu, Jiacheng; Crawford, Forrest W.; Kim, David A.; Stafford, Derek; Christakis, Nicholas A. (2018). "Exposure, hazard, and survival analysis of diffusion on social networks." Statistics in Medicine 37(17): 2561-2585.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/144676
dc.publisherWiley Periodicals, Inc.
dc.publisherSimon & Schuster
dc.subject.othersocial network
dc.subject.otherdiffusion of innovations
dc.subject.othercompeting risks
dc.titleExposure, hazard, and survival analysis of diffusion on social networks
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144676/1/sim7658_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144676/2/sim7658.pdf
dc.identifier.doi10.1002/sim.7658
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceCentola D. An experimental study of homophily in the adoption of health behavior. Science. 2011; 334 ( 6060 ): 1269 ‐ 1272.
dc.identifier.citedreferenceCoviello L, Sohn Y, Kramer AD, et al. Detecting emotional contagion in massive social networks. PloS One. 2014; 9 ( 3 ): e90315.
dc.identifier.citedreferenceHill AL, Rand DG, Nowak MA, Christakis NA. Emotions as infectious diseases in a large social network: the SISa model. Proc R Soc London B: Biol Sci. 2010; 277 ( 1701 ): 3827 ‐ 3835.
dc.identifier.citedreferenceVanderWeele TJ, An W. Social networks and causal inference. Handbook of Causal Analysis for Social Research. New York, NY: Springer; 2013: 353 ‐ 374.
dc.identifier.citedreferenceCrawford FW. The graphical structure of respondent‐driven sampling. Sociol Method. 2016; 46 ( 1 ): 187 ‐ 211.
dc.identifier.citedreferenceCox DR. Regression models and life tables (with discussion). J R Stat Soc. 1972; 34: 187 ‐ 220.
dc.identifier.citedreferenceKlein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer Science & Business Media; 2005.
dc.identifier.citedreferenceFeld SL. Why your friends have more friends than you do. Am J Sociol. 1991; 96 ( 6 ): 1464 ‐ 1477.
dc.identifier.citedreferenceKaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53 ( 282 ): 457 ‐ 481.
dc.identifier.citedreferenceAkaike H. Likelihood of a model and information criteria. J Econometrics. 1981; 16 ( 1 ): 3 ‐ 14.
dc.identifier.citedreferenceAalen OO. A linear regression model for the analysis of life times. Stat Med. 1989; 8 ( 8 ): 907 ‐ 925.
dc.identifier.citedreferenceAral S, Walker D. Creating social contagion through viral product design: a randomized trial of peer influence in networks. Manage Sci. 2011; 57 ( 9 ): 1623 ‐ 1639.
dc.identifier.citedreferenceRand DG, Arbesman S, Christakis NA. Dynamic social networks promote cooperation in experiments with humans. Proc Nat Acad Sci. 2011; 108 ( 48 ): 19193 ‐ 19198.
dc.identifier.citedreferenceOnnela JP, Christakis NA. Spreading paths in partially observed social networks. Phys Rev E. 2012; 85 ( 3 ): 036106.
dc.identifier.citedreferenceHudgens MG, Halloran ME. Causal vaccine effects on binary postinfection outcomes. J Am Stat Assoc. 2006; 101 ( 473 ): 51 ‐ 64.
dc.identifier.citedreferenceRosenbaum PR. Interference between units in randomized experiments. J Am Stat Assoc. 2012; 102 ( 477 ): 191 ‐ 200.
dc.identifier.citedreferenceHudgens MG, Halloran ME. Toward causal inference with interference. J Am Stat Assoc. 2008; 103 ( 482 ): 832 ‐ 842.
dc.identifier.citedreferenceChristakis NA, Fowler JH. Social contagion theory: examining dynamic social networks and human behavior. Stat Med. 2013; 32 ( 4 ): 556 ‐ 577.
dc.identifier.citedreferenceStaples PC, Ogburn EL, Onnela JP. Incorporating contact network structure in cluster randomized trials. Sci Rep. 2015; 5: 17581.
dc.identifier.citedreferenceToulis P, Kao EK. Estimation of causal peer influence effects. In: Atlanta, Georgia, USA: ICML (3); 2013: 1489 ‐ 1497.
dc.identifier.citedreferenceAronow PM, Samii C. Estimating average causal effects under general interference, with application to a social network experiment. Ann Appl Stat. 2017; 11 ( 4 ): 1912 ‐ 1947.
dc.identifier.citedreferenceOgburn EL, VanderWeele TJ, et al. Causal diagrams for interference. Stat Sci. 2014; 29 ( 4 ): 559 ‐ 578.
dc.identifier.citedreferenceFu F, Christakis NA, Fowler JH. Dueling Biological and Social Contagions, Scientific Reports 7: 43634 (March 2017 ). https://doi.org/10.1038/srep43634
dc.identifier.citedreferenceAkaike H. Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike: Springer; 1998: 199 ‐ 213.
dc.identifier.citedreferenceCai C, Zou Y, Peng Y, Zhang J. smcure: an r‐package for estimating semiparametric mixture cure models. Comput Methods Programs Biomed. 2012; 108 ( 3 ): 1255 ‐ 1260.
dc.identifier.citedreferenceValente TW. Network models of the diffusion of innovations. Comput Math Organiz Theory. 1996; 2 ( 2 ): 163 ‐ 164.
dc.identifier.citedreferenceRogers EM. Diffusion of Innovations. New York: Simon & Schuster; 2010.
dc.identifier.citedreferenceRyan B, Gross NC. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociol. 1943; 8 ( 1 ): 15 ‐ 24.
dc.identifier.citedreferenceColeman JS, Katz E, Menzel H, et al. Medical Innovation: A Diffusion Study. New York: Bobbs‐Merrill Company Indianapolis; 1966.
dc.identifier.citedreferenceBurt RS. Social contagion and innovation: cohesion versus structural equivalence. Am J Sociol. 1987; 92 ( 6 ): 1287 ‐ 1335.
dc.identifier.citedreferenceVan den Bulte C, Lilien GL. Medical innovation revisited: social contagion versus marketing effort. Am J Sociol. 2001; 106 ( 5 ): 1409 ‐ 1435.
dc.identifier.citedreferenceFriedkin NE. A multilevel event history model of social diffusion: medical innovation revisited. J Math Sociol. 2010; 34 ( 2 ): 146 ‐ 155.
dc.identifier.citedreferenceBanerjee A, Chandrasekhar AG, Duflo E, Jackson MO. The diffusion of microfinance. Science. 2013; 341 ( 6144 ):1236498.
dc.identifier.citedreferenceValente TW. Network interventions. Science. 2012; 337 ( 6090 ): 49 ‐ 53.
dc.identifier.citedreferenceValente TW, Ritt‐Olson A, Stacy A, Unger JB, Okamoto J, Sussman S. Peer acceleration: effects of a social network tailored substance abuse prevention program among high‐risk adolescents. Addiction. 2007; 102 ( 11 ): 1804 ‐ 1815.
dc.identifier.citedreferenceCentola D. The spread of behavior in an online social network experiment. Science. 2010; 329 ( 5996 ): 1194 ‐ 1197.
dc.identifier.citedreferencePark J, Chung K, Han D, Lee S. Mothers clubs and family planning in Korea. Seoul Korea Seoul National University School of Public Health May 1974. 312; 1974.
dc.identifier.citedreferenceRogers EM, Kincaid DL. Communication Networks: Toward a New Paradigm for Research. New York: Free Press; 1981.
dc.identifier.citedreferenceValente TW, Watkins SC, Jato MN, Van Der Straten A, Tsitsol LPM. Social network associations with contraceptive use among Cameroonian women in voluntary associations. Social Sci Med. 1997; 45 ( 5 ): 677 ‐ 687.
dc.identifier.citedreferenceWellin E. Water boiling in a Peruvian town. In: Paul BD, ed. Health, Culture and Community. New York: Russell Sage; 1955: 71 ‐ 104.
dc.identifier.citedreferenceGruhl D, Guha R, Liben‐Nowell D, Tomkins A. Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web ACM; 2004; New York: 491 ‐ 501.
dc.identifier.citedreferenceLiben‐Nowell D, Kleinberg J. Tracing information flow on a global scale using internet chain‐letter data. Proc Nat Acad Sci. 2008; 105 ( 12 ): 4633 ‐ 4638.
dc.identifier.citedreferenceCha M, Mislove A, Gummadi KP. A measurement‐driven analysis of information propagation in the Flickr social network. In: Proceedings of the 18th International Conference on World Wide Web ACM; 2009; New York, NY: 721 ‐ 730.
dc.identifier.citedreferenceCha M, Haddadi H, Benevenuto F, Gummadi PK. Measuring user influence in Twitter: the million follower fallacy. ICWSM. 2010; 10: 10 ‐ 17.
dc.identifier.citedreferenceBakshy E, Hofman JM, Mason WA, Watts DJ. Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining ACM; 2011; New York: 65 ‐ 74.
dc.identifier.citedreferenceGonzález‐Bailón S, Borge‐Holthoefer J, Rivero A, Moreno Y. The dynamics of protest recruitment through an online network. Sci Rep. 2011; 1: 197.
dc.identifier.citedreferenceLerman K, Ghosh R, Surachawala T. Social contagion: an empirical study of information spread on Digg and Twitter follower graphs. arXiv preprint arXiv:1202.3162. 2012.
dc.identifier.citedreferenceBass FM. A new product growth for model consumer durables. Manage Sci. 1969; 15 ( 5 ): 215 ‐ 227.
dc.identifier.citedreferenceBorge‐Holthoefer J, Baños RA, González‐Bailón S, Moreno Y. Cascading behaviour in complex socio‐technical networks. J Complex Networks. 2013; 1 ( 1 ): 3 ‐ 24.
dc.identifier.citedreferenceValente TW. Social network thresholds in the diffusion of innovations. Social Networks. 1996; 18 ( 1 ): 69 ‐ 89.
dc.identifier.citedreferenceGuardiola X, Diaz‐Guilera A, Perez CJ, Arenas A, Llas M. Modeling diffusion of innovations in a social network. Phys Rev E. 2002; 66 ( 2 ): 026121.
dc.identifier.citedreferenceWang P, González MC, Hidalgo CA, Barabási AL. Understanding the spreading patterns of mobile phone viruses. Science. 2009; 324 ( 5930 ): 1071 ‐ 1076.
dc.identifier.citedreferenceGranovetter M. Threshold models of collective behavior. Am J Sociol. 1978; 83 ( 6 ): 1420 ‐ 1443.
dc.identifier.citedreferenceGranovetter M, Soong R. Threshold models of diffusion and collective behavior. J Math Sociol. 1983; 9 ( 3 ): 165 ‐ 179.
dc.identifier.citedreferenceGranovetter M, Soong R. Threshold models of interpersonal effects in consumer demand. J Econ Behav Organiz. 1986; 7 ( 1 ): 83 ‐ 99.
dc.identifier.citedreferenceMarsden PV, Podolny J. Dynamic analysis of network diffusion processes. In: Weesie J, Flap H, eds. Social Networks Through Time. Utrecht: ISOR/Rijksuniversiteit Utrecht; 1990: 197 ‐ 214.
dc.identifier.citedreferenceStrang D. From dependency to sovereignty: an event history analysis of decolonization 1870‐1987. Am Sociol Rev. 1990; 55 ( 6 ): 846 ‐ 860.
dc.identifier.citedreferenceStrang D. Adding social structure to diffusion models an event history framework. Sociol Methods Res. 1991; 19 ( 3 ): 324 ‐ 353.
dc.identifier.citedreferenceStrang D, Tuma NB. Spatial and temporal heterogeneity in diffusion. American Journal of Sociology. 1993; 99 ( 3 ): 614 ‐ 639.
dc.identifier.citedreferenceGreve HR, Strang D, Tuma NB. Specification and estimation of heterogeneous diffusion models. Sociol Method. 1995; 25: 377 ‐ 420.
dc.identifier.citedreferenceValente TW, Dyal SR, Chu KH, Wipfli H, Fujimoto K. Diffusion of innovations theory applied to global tobacco control treaty ratification. Social Sci Med. 2015; 145: 89 ‐ 97.
dc.identifier.citedreferenceValente TW. Network models and methods for studying the diffusion of innovations. In: Carrington PJ, Scott J, Wasserman S, eds. Models and Methods in Social Network Analysis. New York, NY: Cambridge University Press; 2005: 98 ‐ 116.
dc.identifier.citedreferenceAllison PD. Discrete‐time methods for the analysis of event histories. Sociol Method. 1982; 13 ( 1 ): 61 ‐ 98.
dc.identifier.citedreferenceMyers DJ. The diffusion of collective violence: infectiousness, susceptibility, and mass media networks. Am J Sociol. 2000; 106 ( 1 ): 173 ‐ 208.
dc.identifier.citedreferenceGreenan CC. Diffusion of innovations in dynamic networks. J R Stat Soc Ser A. 2015; 178 ( 1 ): 147 ‐ 166.
dc.identifier.citedreferenceBond RM, Fariss CJ, Jones JJ, et al. A 61‐million‐person experiment in social influence and political mobilization. Nature. 2012; 489 ( 7415 ): 295 ‐ 298.
dc.identifier.citedreferenceKim DA, Hwong AR, Stafford D, et al. Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. The Lancet. 2015; 386 ( 9989 ): 145 ‐ 153.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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